Journal of Meteorological Research

, Volume 31, Issue 5, pp 931–945 | Cite as

A nowcasting technique based on application of the particle filter blending algorithm

  • Yuanzhao Chen
  • Hongping Lan
  • Xunlai Chen
  • Wenhai Zhang
Regular Article


To improve the accuracy of nowcasting, a new extrapolation technique called particle filter blending was configured in this study and applied to experimental nowcasting. Radar echo extrapolation was performed by using the radar mosaic at an altitude of 2.5 km obtained from the radar images of 12 S-band radars in Guangdong Province, China. The first bilateral filter was applied in the quality control of the radar data; an optical flow method based on the Lucas–Kanade algorithm and the Harris corner detection algorithm were used to track radar echoes and retrieve the echo motion vectors; then, the motion vectors were blended with the particle filter blending algorithm to estimate the optimal motion vector of the true echo motions; finally, semi-Lagrangian extrapolation was used for radar echo extrapolation based on the obtained motion vector field. A comparative study of the extrapolated forecasts of four precipitation events in 2016 in Guangdong was conducted. The results indicate that the particle filter blending algorithm could realistically reproduce the spatial pattern, echo intensity, and echo location at 30- and 60-min forecast lead times. The forecasts agreed well with observations, and the results were of operational significance. Quantitative evaluation of the forecasts indicates that the particle filter blending algorithm performed better than the cross-correlation method and the optical flow method. Therefore, the particle filter blending method is proved to be superior to the traditional forecasting methods and it can be used to enhance the ability of nowcasting in operational weather forecasts.

Key words

radar echo particle filter blending bilateral filter semi-Lagrangian extrapolation nowcasting 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arulampalam, M. S., S. Maskell, N. Gordon, et al., 2002: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing, 50, 174–188, doi: 10.1109/78.978374.CrossRefGoogle Scholar
  2. Cao, C. Y., Y. Z. Chen, D. H. Liu, et al., 2015: The optical flow method and its application to nowcasting. Acta Meteor. Sinica, 73, 471–480, doi: 10.11676/qxxb2015.034. (in Chinese)Google Scholar
  3. Carpenter, J. R., P. Clifford, and P. Fearnhead, 1997: Efficient Implementation of Particle Filters for Non-linear Systems. The 4th Interim Report, DRA contract WSS/U1172. Department of Statistics, Oxford University, Oxford, 639 pp.Google Scholar
  4. Chen, M. X., Y. C. Wang, and X. D. Yu, 2007: Improvement and Application Test of TREC Algorithm for Convective Storm Nowcast. J. Appl. Meteor. Sci., 18, 690–701. (in Chinese)Google Scholar
  5. Chen, Z., M. T. Gao, and Y. W. Shen, 2003: Optical flow estimation based on corner point tracking. Computer Engineering, 29, 76–78. (in Chinese)Google Scholar
  6. Cheung, P., and H. Y. Yeung, 2012: Application of optical-flow technique to significant convection nowcast for terminal areas in Hong Kong. The 3rd WMO International Symposium on Nowcasting and Very Short-Range Forecasting (WSN12). Rio de Janeiro, Brazil, 6–10.Google Scholar
  7. Crane, R. K., 1979: Automatic cell detection and tracking. IEEE Trans. Geosci. Electron., 17, 250–262, doi: 10.1109/TGE.1979.294654.CrossRefGoogle Scholar
  8. Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797, doi: 10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.CrossRefGoogle Scholar
  9. Gao, Q. J., P. Xu, and L. Yang, 2012: Breakage detection for grid images based on improved Harris corner. J. Comput. Appl., 32, 766–769. doi: 10.1109/TMEE.2011.6199689. (in Chinese)Google Scholar
  10. Gong, Y. S., 2010: Research on particle filtering algorithms and application in GPS/DR integrated navigation. Ph. D. dissertation, PLA Information Engineering University, Zhengzhou, China, 27 pp. (in Chinese)Google Scholar
  11. Han, L., H. Q. Wang, and Y. J. Lin, 2008: Application of optical flow method to nowcasting convective weather. Acta Scientiarum Naturalium Universitatis Pekinensis, 44, 751–755. (in Chinese)Google Scholar
  12. Johnson, J. T., P. L. MacKeen, A. Witt, et al., 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263–276, doi: 10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.CrossRefGoogle Scholar
  13. Li, T. C., H. Q. Fan, and S. D. Sun, 2015: Particle filtering: Theory, approach, and application for multitarget tracking. Acta Automatica Sinica, 41, 1981–2002, doi: 10.16383/j.aas.2015.c150426. (in Chinese)Google Scholar
  14. Ligda, M. A., 1953: The Horizontal Motion of Small Precipitation Areas as Observed by Radar. Technical Report 21, Department of Meteorology. M.I.T., Cambridge Massachusetts, 60 pp.Google Scholar
  15. Lucas, B. D., and T. Kanade, 1981: An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence. San Francisco, California, USA, 121–130.Google Scholar
  16. Lyu, D. C., J. T. Fan, G. W. Han, et al., 2013: A review of particle filters. Astronomical Res. Technol., 10, 397–409, doi: 10.14005/j.cnki.issn1672-7673.2013.04.005. (in Chinese)Google Scholar
  17. Mueller, C., T. Saxen, R. Roberts, et al., 2003: NCAR auto-nowcast system. Wea. Forecasting, 18, 545–561, doi: 10.1175/1520-0434(2003)018<0545:NAS>2.0.CO;2.CrossRefGoogle Scholar
  18. Overton, K. J., and T. E. Weymouth, 1979: A noise reducing preprocessing algorithm. Proceedings of IEEE Computer Science Conference on Pattern Recognition and Image Processing. IEEE, Chicago, USA, 498–507.Google Scholar
  19. Rasmussen, R., M. Dixon, F. Hage, et al., 2001: Weather support to deicing decision making (WSDDM): A winter weather nowcasting system. Bull. Amer. Meteor. Soc., 82, 579–596, do i1:0.1175/1520-0477(2001)082<0579:WSTDDM>2.3.CO;2.CrossRefGoogle Scholar
  20. Reich, S., 2007: An explicit and conservative remapping strategy for semi-Lagrangian advection. Atmos. Sci. Lett., 8, 58–63, doi: 10.1002/asl.151.CrossRefGoogle Scholar
  21. Rinehart, R. E., and E. T. Garvey, 1978: Three-dimensional storm motion detection by conventional weather radar. Nature, 273, 287–289, doi: 10.1038/273287a0.CrossRefGoogle Scholar
  22. Si, S. H., F. Y. Hu, Y. J. Gu, et al., 2014: Improved denoising algorithm based on non-regular area Gaussian filtering. Computer Science, 41, 313–316, doi: 10.11896/j.issn.1002-137X.2014.11.062. (in Chinese)Google Scholar
  23. Tian, H., J. C. Bian, and H. Yan, 2004: Parallelization of the semi-Lagrangian shallow-water model using MPI techniques. J. Appl. Meteor. Sci., 15, 417–426. (in Chinese)Google Scholar
  24. Wang, E. S., T. Pang, P. P. Qu, et al., 2015: RAIM algorithm based on particle filter and likelihood ratio method. J. Nanjing Univ. Aeronautics & Astronautics, 47, 46–51, doi: 10.16356/j.1005-2615.2015.01.006. (in Chinese)Google Scholar
  25. Wang, G., J. L. Wang, and B. L. Su, 2013: Dynamic simulation and prediction of ecological footprint in Liaohe River basin based on ARIMA model. Ecol. Environ. Sci., 22, 632–638, doi: 10.16258/j.cnki.1674-5906.2013.04.001. (in Chinese)Google Scholar
  26. Wang, G. L., C. G. Zhao, L. P. Liu, et al., 2013: Error analysis of radar echo extrapolation. Plateau Meteor., 32, 874–883, doi: 10.7522/issn,100-0534.2012.00081. (in Chinese)Google Scholar
  27. Wilson, J. W., N. A. Crook, C. K. Mueller, et al., 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79, 2079–2100, doi: 10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2.CrossRefGoogle Scholar
  28. Wilson, J. W., Y. R. Feng, M. Chen, et al., 2010: Nowcasting challenges during the Beijing Olympics: Successes, failures, and implications for future nowcasting systems. Wea. Forecasting, 25, 1691–1714, doi: 10.1175/2010WAF2222417.1.CrossRefGoogle Scholar
  29. Xiao, Y. J., and L. P. Liu, 2006: Study of methods for interpolating data from weather radar network to 3-D grid and mosaics. Acta Meteor. Sinica, 64, 647–657, doi: 10.11676/qxxb2006.063. (in Chinese)Google Scholar
  30. Yu, X. D., X. G. Zhou, and X. M. Wang, 2012: The advances in the nowcasting techniques on thunderstorms and severe convection. Acta Meteor. Sinica, 70, 311–337, doi: 10.11676/qxxb2012.030. (in Chinese)Google Scholar
  31. Zeng, X. T., Q. Q. Liang, M. S. Nong, et al., 2010: Application test of TREC algorithm to severe convective storm nowcasting. Meteor. Mon., 36, 31–40, doi: 10.7519/j.issn.1000-0526.2010.1.005. (in Chinese)Google Scholar
  32. Zhang, L. F., H. G. Wei, M. H. Li, et al., 2014: The compatibility of Astragalus Membranaceus and Salvia Miltiorrhiza Extract control preliminary study of lipid metabolism in rats. Sci. Technol. Engineering, 14, 145–148. (in Chinese)Google Scholar
  33. Zhang, X., W. Huang, and B. D. Chen, 2015: Implementation of a 2nd order semi-implicit semi-Lagrangian trajectory algorithm and time integration scheme in the GRAPES regional model. Acta Meteor. Sinica, 73, 557–565, doi: 10.11676/qxxb2015.023. (in Chinese)Google Scholar
  34. Zhao, Y., J. H. Chen, J. J. Zhang, et al., 2007: Weather radar echo image processing based on median filter and wavelet transform. Scientia Meteor. Sinica, 27, 63–68. (in Chinese)Google Scholar
  35. Zheng, Y. G., K. H. Zhou, J. Sheng, et al., 2015: Advances in techniques of monitoring, forecasting and warning of severe convective weather. J. Appl. Meteor. Sci., 26, 641–657, doi: 10.11898/1001-7313.20150601. (in Chinese)Google Scholar
  36. Zhou, Y. W., Q. Chen, Q. S. Sun, et al., 2014: Remote sensing image enhancement based on dark channel prior and bilateral filtering. J. Image Graphics, 19, 313–321. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Yuanzhao Chen
    • 1
    • 2
  • Hongping Lan
    • 2
  • Xunlai Chen
    • 1
    • 2
  • Wenhai Zhang
    • 3
  1. 1.Meteorological Bureau of Shenzhen MunicipalityShenzhenChina
  2. 2.Shenzhen Key Laboratory of Severe Weather in South ChinaShenzhenChina
  3. 3.Shenzhen Academy of Severe Storm ScienceShenzhenChina

Personalised recommendations